INTERNATIONAL JOURNAL OF MOlecular medicine 43: 1643-1656, 2019

Bioinformatic analysis of next‑generation sequencing data to identify dysregulated in fibroblasts of idiopathic pulmonary fibrosis

CHAU‑CHYUN SHEU1‑3, WEI‑AN CHANG1,2, MING‑JU TSAI1‑3, SSU‑HUI LIAO1, INN‑WEN CHONG2,3 and PO‑LIN KUO1

1Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University; 2Division of Pulmonary and Critical Care Medicine, Kaohsiung Medical University Hospital; 3Department of Internal Medicine, School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan, R.O.C.

Received October 7, 2018; Accepted January 29, 2019

DOI: 10.3892/ijmm.2019.4086

Abstract. Idiopathic pulmonary fibrosis (IPF) is a lethal and miRNA expression data, combined with GEO verifica- fibrotic lung disease with an increasing global burden. It is tion, finally identified Homo sapiens (hsa)‑miR‑1254‑INKA2 hypothesized that fibroblasts have a number of functions that and hsa‑miR‑766‑3p‑INKA2 as the potential miRNA‑mRNA may affect the development and progression of IPF. However, interactions in IPF fibroblasts. In summary, the results the present understanding of cellular and molecular mecha- of the present study suggest that dysregulation of PAX8, nisms associated with fibroblasts in IPF remains limited. hsa‑miR‑1254‑INKA2 and hsa‑miR‑766‑3p‑INKA2 may The present study aimed to identify the dysregulated genes promote the proliferation and survival of IPF fibroblasts. In in IPF fibroblasts, elucidate their functions and explore the functional analysis of the dysregulated genes, a marked potential microRNA (miRNA)‑mRNA interactions. mRNA association between fibroblasts and the ECM was identified. and miRNA expression profiles were obtained from IPF These data improve the current understanding of fibroblasts fibroblasts and normal lung fibroblasts using a next‑gener- as key cells in the pathogenesis of IPF. As a screening study ation sequencing platform, and bioinformatic analyses were using bioinformatics approaches, the results of the present performed in a step‑wise manner. A total of 42 dysregulated study require additional validation. genes (>2 fold‑change of expression) were identified, of which 5 were verified in the Expression Omnibus (GEO) Introduction database analysis, including the upregulation of neurotrimin (NTM), paired box 8 (PAX8) and mesoderm development Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, LRP chaperone, and the downregulation of ITPR interacting fibrotic lung disease. IPF predominantly affects elderly men. domain containing 2 and Inka box actin regulator 2 (INKA2). Patients with IPF usually present with exertional dyspnea, dry Previous data indicated that PAX8 and INKA2 serve roles in cough and inspiratory bibasilar Velcro‑like crackles on lung cell growth, proliferation and survival. analysis auscultation. At present, high‑resolution computed tomography indicated that the most significant function of these 42 dysreg- is the most important diagnostic tool for IPF, which typically ulated genes was associated with the composition and function presents with usual interstitial pneumonia pattern (1,2). IPF is of the extracellular matrix (ECM). A total of 60 dysregulated a lethal lung disease. The prognosis of IPF is poorer compared miRNAs were also identified, and 1,908 targets were predicted with a number of types of cancer (3), with an estimated median by the miRmap database. The integrated analysis of mRNA survival of 3‑5 years if untreated (2). The incidence of IPF, hospitalization rates and mortality due to IPF have increased during previous years, suggesting an increasing global burden of this disease (4‑6). The pathogenesis of IPF remains largely unknown. Data Correspondence to: Professor Po‑Lin Kuo, Graduate Institute from observational studies suggest that cigarette smoking, of Clinical Medicine, College of Medicine, Kaohsiung Medical air pollution (7), environmental exposure (8), chronic viral University, 100 Shih‑Chuan 1st Road, Kaohsiung 807, Taiwan, infection (9) and esophageal reflux (10) predispose individuals R.O.C. E‑mail: [email protected] to IPF (4). There is also increasing evidence for genetic predisposition to IPF: Recent genome‑wide association studies Key words: bioinformatics, fibroblasts, idiopathic pulmonary identified that several genetic variants, primarily involved in fibrosis, Inka box actin regulator 2, next‑generation sequencing, epithelial cell‑cell adhesion and integrity, the innate immune paired box 8 response, host defense and DNA repair, are associated with increased risk of IPF (11‑13). These together indicate that gene‑environmental interactions serve important roles in the 1644 SHEU et al: BIOINFORMATIC ANALYSIS OF DYSREGULATED GENES IN IPF FIBROBLASTS pathogenesis of IPF. In genetically predisposed individuals, DHLF‑IPF and NHLF cells used for the NGS analysis were repetitive environmental exposures lead to an aberrant harvested from the 1st generation of cells following cultivation injury‑remodeling process (14). Activated lung epithelial cells from the primary cells. produce pro‑fibrotic growth factors, chemokines and other mediators, resulting in abnormal wound healing character- NGS. The expression profiles of miRNAs and mRNAs were ized by mesenchymal transition of epithelial cells, activation examined using NGS. Protocol was described in our previous and differentiation of fibroblasts and myofibroblasts. These studies (25‑27). In brief, total RNA from the DHLF‑IPF and fibroblasts and myofibroblasts secrete excessive amounts of NHLF cells were extracted with TRIzol® reagent (Thermo extracellular matrix including fibrillary collagens, Fisher Scientific, Inc., Waltham, MA, USA) at Welgene Biotech vimentin and fibronectin (15), contributing to the destruction Co., Ltd. (Taipei, Taiwan). Purified RNA was quantified at an of lung architecture by progressive scarring. optical density of 260 nm using an ND‑1000 spectrophotom- It is hypothesized that fibroblasts have a number of func- eter (NanoDrop Technologies; Thermo Fisher Scientific, Inc.) tions that may affect the development and progression of and the RNA quality was examined using a Bioanalyzer 2100 IPF (16). Although previous studies have described the differ- (Agilent Technologies, Inc., Santa Clara, CA, USA) with ences between IPF and normal lung fibroblasts (17‑21), the RNA 6000 LabChip® kit (Agilent Technologies, Inc.). Next, the knowledge concerning the cellular and molecular mechanisms samples were prepared for library construction and sequencing associated with fibroblasts in IPF remains limited, and their using an Illumina preparation kit (Illumina, Inc., San Diego, specific roles requires additional investigation (16,22). CA, USA). For small RNA sequencing, the harvested cDNA For an improved understanding of the roles fibroblasts constructs containing 18‑40‑nucleotide RNA fragments serve in the pathogenesis of IPF, the present study used a next (140‑155 nucleotides in length with all adapters) were selected. generation sequencing (NGS) platform and a step‑wise bioin- The libraries were then sequenced on an Illumina instrument formatic approach to analyze the expression levels of mRNAs (75 single‑end cycles). Following trimming and removal and microRNAs (miRNAs) and their interactions in IPF fibro- of reads with low quality scores using Trimmomatics (28), blasts compared with normal lung fibroblasts. The aims of the the qualified reads were analyzed with miRDeep2 (29) and present study were to identify the top differentially expressed the UCSC Genome Browser (genome.ucsc.edu/) (30). The genes and subsequently elucidate the function of these miRNAs with low levels (<1 normalized read per million) in dysregulated genes, and to explore potential miRNA‑mRNA either DHLF‑IPF or NHLF cells were excluded. interactions in IPF fibroblasts. For transcriptome sequencing, the library was constructed with a SureSelect Strand Specific RNA Library Preparation Materials and methods kit (Agilent Technologies, Inc.), and sequenced on a Solexa platform (150 paired‑end cycles), using the TruSeq SBS kit Study design. The study design is summarized in Fig. 1. (Illumina, Inc.). Similar with small RNA sequencing data, Firstly, cell cultures of the IPF fibroblasts and healthy lung Trimmomatics (version 0.36) was implemented to trim or fibroblasts were generated. Then, total RNA of the fibroblasts remove the reads with low quality score. Qualified reads were were extracted and sent for RNA and small RNA sequencing analyzed using HISAT2 (version 2.1.0) (31). The genes with low using the NGS platform. The differentially expressed genes expression levels [<0.3 fragment per kilobase of transcript per (>2 fold‑change) were analyzed with Ingenuity® Pathway million mapped reads (FPKM)] in either DHLF‑IPF or NHLF Analysis (IPA) and the Database for Annotation, Visualization cells were excluded. The 2‑tailed P‑values were calculated and Integrated Discovery (DAVID) for pathway analysis and [2(Cufflinks version 2.2.1)] with non‑grouped samples used functional interpretation. These dysregulated genes were addi- ‘blind mode’, in which all samples were treated as replicates tionally verified in representative Gene Expression Omnibus of a single global ‘condition’ and used to build one model for

(GEO) datasets. The differentially expressed miRNAs statistical analysis (32,33). Genes with P<0.05 [(‑log10) P‑value (>2 fold‑change) were analyzed with miRmap (mirmap.ezlab. >1.3] and >2 fold‑changes were considered to be significantly org) (23) for target prediction. Then, Venn diagrams were used dysregulated genes. to determine genes with potential miRNA‑mRNA interactions. These potential miRNA‑mRNA interactions were confirmed miRmap and TargetScan database analysis. miRmap is an using a second miRNA prediction database, TargetScan open‑source software library, which provides comprehensive (www.targetscan.org) (24). Finally, a literature search for the miRNA‑target prediction (23). By calculating the complemen- functions of these dysregulated genes was performed, and a tary ability of miRNA‑mRNA interactions, the putative target hypothesis was generated. genes of each miRNA may be identified. By combining ther- modynamic, evolutionary, probabilistic and sequence‑based Culture of primary cells. Diseased human lung fibroblasts features, the predictor also estimates mRNA‑repression (DHLF)‑IPF (cat. no. CC‑7231) and normal human lung fibro- strength for ranking potential candidate targets. The predic- blasts (NHLF; cat. no. CC‑2512) were purchased from Lonza tion produces a list of putative target genes with an miRmap Walkersville Inc. (Walkersville, MD, USA). The cells were score, a predictive reference value. In the present study, an grown in FGW™‑2 Fibroblast Growth Medium‑2 Bulletkit™ miRmap score ≥99 was used as the criterion for selecting (Lonza Walkersville Inc.; cat. no. CC‑3132), containing 0.5 ml putative miRNA targets. human fibroblast growth factor‑basic, 0.5 ml insulin, 10 ml TargetScan is an online database for predicting biological fetal bovine serum and 0.5 ml GA‑1000. To avoid losing targets of miRNAs (24). It searches for the presence of any original characteristics over multiple generations, the conserved 8, 7 and 6 mer sites, which match the seed region of INTERNATIONAL JOURNAL OF MOlecular medicine 43: 1643-1656, 2019 1645 each miRNA. Predictions are ranked based on the predicted difference. Dysregulated genes identified from the IPF and targeting efficacy or the probability of conserved targeting. normal fibroblasts NGS analysis were considered verified if they met the following criteria: Exhibiting concordant IPA. IPA (Qiagen Inc., Valencia, CA, USA) (34) is a web‑based up‑/downregulation in the GSE24206 and GSE44723 datasets, software application for the analysis, integration and interpre- and demonstrating significantly different expression between tation of data derived from ‘omics experiments, including RNA patients with IPF and controls or across three groups in at least sequencing, small RNA sequencing, microarrays, metabolo- one dataset. mics and proteomics. IPA enables the rapid understanding and visualization of data, and provides a range of data in addition Results to pathway analysis, including the identification of key regula- tors and activity to explain expression patterns, prediction of Gene expression profiles and miRNA changes in IPF downstream effects on biological and disease processes, and fibroblasts. Samples from DHLF‑IPF and NHLF cells provision of targeted data on genes, proteins, chemicals and were sequenced for mRNA and small RNA using an NGS drugs. The dysregulated genes from the IPF fibroblasts were platform. The volcano plot of dysregulated genes in fibro- uploaded to IPA (version 2.3) to identify the top canonical blasts of patients with IPF (DHLF‑IPF cells) compared with pathways, upstream regulators, and molecular and cellular normal controls (NHLF cells) is presented in Fig. 2A. The functions. red nodes represent the significantly upregulated genes, and the green nodes represent significantly downregulated genes. DAVID database analysis. DAVID is a powerful gene func- The density plot of the deep sequencing results between the tional classification tool that integrates multiple functional DHLF‑IPF and NHLF cells is presented in Fig. 2B. The gene annotation databases, including Gene Ontology (GO) and expression of DHLF‑IPF cells demonstrated increased FPKM Kyoto Encyclopedia of Genes and Genomes databases values and increased density compared with NHLF cells. Gene (https://david.ncifcrf.gov/) (35,36). By calculating the simi- expression analysis revealed 42 differentially expressed genes larity of global annotation profiles with an agglomeration with fold‑change >2, including 23 upregulated and 19 down- algorithm method, a list of notable genes may be classified regulated genes (Table I). miRNA expression analysis revealed into clusters of associated ‘Biological Process, ‘Cellular 60 miRNAs with fold‑change >2, including 43 upregulated and Components’, and ‘Molecular Functions’. It also provides an 17 downregulated miRNAs (Table II). According to miRmap Expression Analysis Systematic Explorer (EASE) score, a analysis, 1,908 targets were predicted from these dysregulated modified one‑tailed Fisher's Exact P‑value, for analysis. The miRNAs. reference score represents how specifically the user genes are involved in the category. An EASE score=0.1 was selected as IPA and DAVID analysis of dysregulated genes in IPF the default, and =1 to extend clustering range in the analysis in fibroblasts. The pathways and functional annotation of the the present study. 42 dysregulated genes in IPF fibroblasts were first analyzed using IPA. The results are summarized in Table III. The top GEO database analysis. The GEO is a web database that canonical pathways were cAMP‑mediated signaling, GP6 collects submitted high‑throughput gene‑expression data of signaling pathway, cardiac‑adrenergic signaling, hepatic microarrays, chips or NGS (www.ncbi.nlm.nih.gov/geo) (37). fibrosis/hepatic stellate cell activation and calcium transport I. Microarrays of accession numbers GSE24206, which included The top upstream regulators were fibrillin‑1, bromodo- whole lung tissue samples from patients with early and main‑containing 4, transcription factor GATA‑6, TSIX advanced IPF (38) and GSE44723, which included samples and DNA‑binding protein RFX5. Fig. 4 demonstrates the of cultured lung fibroblasts from patients with IPF (39), were network of these top 5 upstream regulators and dysregulated used in the present study. GSE44723 assessed gene expression genes. The top molecular and cellular functions included cell in fibroblasts obtained from 4 normal controls, 4 patients with cycle, cell morphology, cellular development, lipid metabolism rapidly progressing IPF and 6 patients with slowly progressing and molecular transport. IPF. GSE24206 assessed gene expression in whole lung biopsy The functional annotation of the 42 dysregulated genes or explant samples from 6 healthy controls, 8 patients with was subsequently analyzed using DAVID. Notably, results early IPF and 9 patients with advanced IPF. The databases used within the ‘Biological Process’, ‘Cellular Component’ and the platform of Affymetrix U133 Plus 2.0 ‘Molecular Function’ categories all indicated that the most Array. Raw data extracted from the GEO were replotted significant function of these dysregulated genes was associ- using GraphPad Prism 7 software (GraphPad Software, Inc., ated with the composition and function of the extracellular La Jolla, CA, USA). matrix (ECM) (Fig. 3).

Statistical analysis. Statistical analyses were performed using GEO database analysis of dysregulated genes in IPF fibro‑ SAS 9.4 software (SAS Institute Inc., Cary, NC, USA). To iden- blasts. To additionally verify the 42 dysregulated genes in tify the differentially expressed genes, the Wilcoxon rank‑sum clinical samples from patients with IPF, the GEO database test was used to compare gene expressions between two groups was searched and two representative microarray datasets (patients with IPF vs. controls), and the Kruskal‑Wallis test were selected: GSE44723 (IPF cultured lung fibroblasts) followed by Benjamini‑Hochberg multiple‑testing corrections and GSE24206 (early and advanced IPF whole lung tissues). was used to compare gene expressions across three groups. Dysregulated genes were considered verified if they exhibited P<0.05 was considered to indicate a statistically significant concordant up‑/downregulation in the two GSE44723 and 1646 SHEU et al: BIOINFORMATIC ANALYSIS OF DYSREGULATED GENES IN IPF FIBROBLASTS

Figure 1. Schematic illustration of the study design. Following culture of the IPF fibroblasts and healthy lung fibroblasts, the total RNA were extracted and deep sequenced using a next‑generation sequencing platform. The differentially expressed genes (>2 fold‑change) were identified, and analyzed with IPA and DAVID for pathway analysis and functional interpretation. The GEO IPF databases were analyzed to confirm dysregulated genes identified in the present study. Conversely, the differentially expressed miRNAs (>2 fold‑change) were analyzed with miRmap for target prediction. Then, genes with potential miRNA‑mRNA interactions were determined by Venn diagram analysis. These miRNA‑mRNA interactions were verified by a second miRNA prediction database, TargetScan. Finally, a literature search was performed and a hypothesis was generated. IPF, idiopathic pulmonary fibrosis; IPA, Ingenuity® Pathway Analysis; DAVID, Database for Annotation, Visualization, and Integrated Discovery; GEO, Gene Expression Omnibus.

Figure 2. Differential gene expression patterns between IPF and normal fibroblasts. (A) The volcano plot of ‑log10 (P‑value) vs. log2 (fold‑change) demonstrated differentially expressed genes in DHLF‑IPF vs. NHLF. Genes with ‑log10 (P‑value) >1.3 and >2 fold‑changes are plotted in red (upregulated genes) or green (downregulated genes). (B) The frequency distribution of FPKM between DHLF‑IPF cells and NHLF cells was compared and presented in the density plot. IPF, idiopathic pulmonary fibrosis; FPKM, fragments per kilobase of transcript per million mapped reads; DHLF, diseased human lung fibroblasts; NHLF, normal human lung fibroblasts. INTERNATIONAL JOURNAL OF MOlecular medicine 43: 1643-1656, 2019 1647

Table I. Dysregulated gene in IPF fibroblasts compared with normal lung fibroblasts.

A, Upregulated

Gene Full gene name DHLF‑IPF FPKM NHLF FPKM Fold‑change P‑value

HSPA12B Heat shock protein family A (Hsp70) 59.62 3.28 18.18 0.005 member 12B NTM Neurotrimin 117.08 8.40 13.94 0.032 PAX8 Paired box 8 86.90 13.69 6.35 0.000 MKI67 Marker of proliferation Ki‑67 16.75 3.25 5.15 0.045 POSTN Periostin 42.91 8.35 5.14 0.024 MYO1C Myosin IC 21.87 4.34 5.04 0.020 LINC01529 Long intergenic non‑protein 41.54 12.83 3.24 0.007 coding RNA 1529 COL1A2 Collagen type I alpha 2 chain 69.47 23.42 2.97 0.015 CCNL1 Cyclin L1 38.61 13.41 2.88 0.031 ATP2B4 ATPase plasma membrane Ca2+ 29.61 10.64 2.78 0.017 transporting 4 ANP32B Acidic nuclear phosphoprotein 32 52.75 19.24 2.74 0.037 family member B PKMYT1 Protein kinase, membrane associated 571.51 212.09 2.69 0.032 tyrosine/threonine 1 GAB2 GRB2-associated binding protein 2 23.34 8.87 2.63 0.005 GPM6B Glycoprotein M6B 39.57 15.38 2.57 0.029 TDG Thymine DNA glycosylase 32.79 13.38 2.45 0.048 HSPA8 Heat shock protein family A (Hsp70) 94.93 39.54 2.40 0.041 member 8 CD248 CD248 molecule 25.01 10.79 2.32 0.036 LINC00847 Long intergenic non‑protein coding 91.64 41.90 2.19 0.026 RNA 847 ANKRD18EP Ankyrin repeat domain 18E, pseudogene 98.99 45.65 2.17 0.022 CNN3 Calponin 3 70.11 33.29 2.11 0.047 MESD Mesoderm development LRP chaperone 93.66 44.85 2.09 0.028 IQGAP1 IQ motif containing GTPase activating 45.40 22.51 2.02 0.023 protein 1 LINC01291 Long intergenic non‑protein coding 161.02 80.15 2.01 0.010 RNA 1291

B, Downregulated

TYW1 tRNA‑YW synthesizing protein 1 24.22 146.89 ‑6.06 0.044 homolog COL4A1 Collagen type IV alpha 1 chain 4.11 23.87 ‑5.81 0.006 AKAP12 A‑kinase anchoring protein 12 4.24 23.09 ‑5.45 0.017 ITPRID2 ITPR interacting domain containing 2 4.96 23.91 ‑4.82 0.042 PRKAR1B Protein kinase cAMP‑dependent type I 30.41 131.62 ‑4.33 0.036 regulatory subunit beta INKA2 Inka box actin regulator 2 11.38 47.14 ‑4.14 0.006 AKAP2 A‑kinase anchoring protein 2 12.30 47.75 ‑3.88 0.002 GPR17 G protein‑coupled receptor 17 14.88 52.29 ‑3.51 0.008 FAM151A Family with sequence similarity 151 11.47 36.15 ‑3.15 0.018 member A UXT‑AS1 UXT antisense RNA 1 48.81 151.15 ‑3.10 0.028 IL17RC Interleukin 17 receptor C 8.18 23.55 ‑2.88 0.033 SPDYE6 Speedy/RINGO cell cycle regulator 10.45 30.07 ‑2.88 0.037 family member E6 1648 SHEU et al: BIOINFORMATIC ANALYSIS OF DYSREGULATED GENES IN IPF FIBROBLASTS

Table I. Continued.

B, Downregulated

Gene Full gene name DHLF‑IPF FPKM NHLF FPKM Fold‑change P‑value

MED31 Mediator complex subunit 31 25.55 69.73 ‑2.73 0.041 FEM1B Fem‑1 homolog B 14.77 35.13 ‑2.38 0.010 NCL Nucleolin 19.51 45.91 ‑2.35 0.030 PTX3 Pentraxin 3 20.35 44.55 ‑2.19 0.042 OR7E12P Olfactory receptor family 7 subfamily E 126.50 268.42 ‑2.12 0.013 member 12 pseudogene SPON1 Spondin 1 16.93 34.91 ‑2.06 0.030 COL4A2 Collagen type IV alpha 2 chain 12.65 25.46 ‑2.01 0.040

IPF, idiopathic pulmonary fibrosis; FPKM, fragments per kilobase of transcript per million mapped reads; DHLF, diseased human lung fibro- blast; NHLF, normal human lung fibroblast.

Figure 3. Gene Ontology analysis of dysregulated genes identified in idiopathic pulmonary fibrosis fibroblasts. Functional annotation of the 42 dysregu- lated genes was determined using Database for Annotation, Visualization, and Integrated Discovery. Results within the (A) Biological Process, (B) Cellular Component and (C) Molecular Function categories all indicated that the most significant function of these genes was associated with the composition and function of extracellular matrix.

Figure 4. Network of top 5 upstream regulators and dysregulated genes in idiopathic pulmonary fibrosis fibroblasts. The top 5 upstream regulators, including FBN1, BRD4, GATA6, TSIX and RFX5, were generated using Ingenuity® Pathway Analysis based on the significance of overlap between the dysregulated genes and the genes that were regulated by the upstream regulators. The dysregulated genes targeted by these top upstream regulators were COL1A2, COL4A1 and COL4A2, all belonging to the fibrillar collagen family. Numbers in parentheses are the number of reports supporting interactions. E, expression; FC, expression fold‑change; PD, protein‑DNA binding; CLO1A2, collagen alpha‑2(I) chain; COL4A1, collagen alpha‑1(IV) chain; COL4A2, collagen alpha‑2(IV) chain; FBN1, fibrillin‑1; BRD4, bromodomain‑containing protein 4; GATA6, transcription factor GATA‑6; RFX5, DNA‑binding protein RFX5. INTERNATIONAL JOURNAL OF MOlecular medicine 43: 1643-1656, 2019 1649

Table II. Dysregulated miRNAs in IPF fibroblasts vs. normal lung fibroblasts. miRNA DHLF‑IPF Seq (norm) NHLF Seq (norm) Fold‑change Up/Down hsa‑miR‑10b‑5p 3,357.09 317.7 10.57 Up hsa‑miR‑412‑5p 50.6 5.86 8.63 Up hsa‑miR‑329‑3p 6.77 1.69 4.01 Up hsa‑miR‑4661‑5p 4.51 1.17 3.85 Up hsa‑miR‑4521 17.7 4.82 3.67 Up hsa‑miR‑3130‑3p 3.8 1.17 3.25 Up hsa‑miR‑369‑3p 26.73 8.34 3.21 Up hsa‑miR‑615‑3p 11.17 3.52 3.17 Up hsa‑miR‑766‑3p 5.58 1.82 3.07 Up hsa‑miR‑548am‑5p 5.11 1.69 3.02 Up hsa‑miR‑5000‑3p 5.46 1.82 3.00 Up hsa‑miR‑619‑5p 3.44 1.17 2.94 Up hsa‑miR‑486‑5p 424.28 145.3 2.92 Up hsa‑miR‑493‑5p 369.05 132.01 2.80 Up hsa‑miR‑3613‑5p 10.45 3.78 2.76 Up hsa‑miR‑625‑3p 5.7 2.08 2.74 Up hsa‑miR‑33b‑5p 4.75 1.82 2.61 Up hsa‑miR‑382‑3p 14.49 5.6 2.59 Up hsa‑miR‑379‑3p 33.38 13.55 2.46 Up hsa‑miR‑199b‑5p 471.8 192.86 2.45 Up hsa‑miR‑127‑3p 292.67 120.67 2.43 Up hsa‑miR‑1254 2.79 1.17 2.38 Up hsa‑miR‑543 31.95 13.42 2.38 Up hsa‑miR‑549a 15.92 6.91 2.30 Up hsa‑miR‑193a‑3p 5.7 2.48 2.30 Up hsa‑miR‑665 24.11 10.56 2.28 Up hsa‑miR‑376c‑3p 20.55 9.12 2.25 Up hsa‑miR‑487a‑3p 32.9 14.99 2.19 Up hsa‑miR‑582‑3p 4.87 2.22 2.19 Up hsa‑miR‑134‑5p 174.73 80.14 2.18 Up hsa‑miR‑1185‑5p 3.68 1.69 2.18 Up hsa‑miR‑493‑3p 159.05 73.89 2.15 Up hsa‑miR‑136‑3p 424.16 198.72 2.13 Up hsa‑miR‑136‑5p 12.35 5.86 2.11 Up hsa‑miR‑1185‑1‑3p 49.06 23.33 2.10 Up hsa‑miR‑654‑3p 1,401.49 669.93 2.09 Up hsa‑miR‑185‑3p 6.18 3 2.06 Up hsa‑miR‑548ao‑3p 2.14 1.04 2.06 Up hsa‑miR‑668‑3p 3.21 1.56 2.06 Up hsa‑miR‑410‑3p 865.2 424.42 2.04 Up hsa‑miR‑409‑3p 3,074.39 1,512.26 2.03 Up hsa‑miR‑494‑3p 6.3 3.13 2.01 Up hsa‑miR‑1197 7.84 3.91 2.01 Up hsa‑miR‑182‑5p 47.63 95.52 ‑2.01 Down hsa‑miR‑422a 1.66 3.39 ‑2.04 Down hsa‑miR‑3200‑3p 3.8 7.95 ‑2.09 Down hsa‑miR‑335‑5p 49.53 103.99 ‑2.10 Down hsa‑miR‑138‑1‑3p 87.18 189.86 ‑2.18 Down hsa‑miR‑365a‑5p 1.43 3.13 ‑2.19 Down hsa‑miR‑5699‑5p 1.07 2.35 ‑2.20 Down hsa‑miR‑664a‑3p 2.38 5.86 ‑2.46 Down hsa‑miR‑1538 2.26 5.6 ‑2.48 Down 1650 SHEU et al: BIOINFORMATIC ANALYSIS OF DYSREGULATED GENES IN IPF FIBROBLASTS

Table II. Continued. miRNA DHLF‑IPF Seq (norm) NHLF Seq (norm) Fold‑change Up/Down hsa‑miR‑4662a‑5p 2.97 7.56 ‑2.55 Down hsa‑miR‑335‑3p 341.14 920.39 ‑2.70 Down hsa‑miR‑29c‑5p 1.19 3.39 ‑2.85 Down hsa‑miR‑9‑5p 1.54 4.69 ‑3.05 Down hsa‑miR‑4461 2.61 7.95 ‑3.05 Down hsa‑miR‑3662 1.43 4.82 ‑3.37 Down hsa‑miR‑4767 1.31 4.82 ‑3.68 Down hsa‑miR‑204‑5p 45.37 170.97 ‑3.77 Down

IPF, idiopathic pulmonary fibrosis; DHLF, diseased human lung fibroblast; NHLF, normal human lung fibroblast; Up, upregulation; Down, downregulation; hsa, Homo sapiens; miR, microRNA.

Table III. Summary of Ingenuity® Pathway Analysis of 42 dysregulated genes in idiopathic pulmonary fibrosis fibroblasts.

Categories P‑value Overlap (%) No. of molecules

Top canonical pathways cAMP‑mediated signaling 0.0007 4/225 (1.8) ‑ GP6 signaling pathway 0.0018 3/131 (2.3) ‑ Cardiac‑adrenergic signaling 0.0021 3/140 (2.1) ‑ Hepatic fibrosis/hepatic stellate cell activation 0.0045 3/183 (1.6) ‑ Calcium transport I 0.0181 1/10 (10.0) ‑ Top upstream regulators FBN1 0.0002 ‑ ‑ BRD4 0.0021 ‑ ‑ GATA6 0.0030 ‑ ‑ TSIX 0.0037 ‑ ‑ RFX5 0.0037 ‑ ‑ Top molecular and cellular function Cell cycle 0.0013‑0.0181 ‑ 6 Cell morphology 0.0018‑0.0395 ‑ 4 Cellular development 0.0018‑0.0106 ‑ 4 Lipid metabolism 0.0018‑0.0199 ‑ 2 Molecular transport 0.0018‑0.0055 ‑ 2

IPA, Ingenuity® Pathway Analysis; IPF, idiopathic pulmonary fibrosis; FBN1, fibrillin‑1; BRD4, bromodomain‑containing protein 4; GATA6, transcription factor GATA‑6; RFX5, DNA‑binding protein RFX5.

GSE24206 datasets, with significantly different expression analysis of the 1,908 target genes of 60 dysregulated miRNAs, between patients with IPF and controls in at least one dataset. predicted from miRmap, and the 42 dysregulated genes revealed The results are summarized in Table IV. Based on the GEO 5 dysregulated genes with potential miRNA‑mRNA interactions analysis, neurotrimin (NTM), paired box 8 (PAX8) and meso- in IPF fibroblasts (Fig. 6). A total of 2 miRNA‑mRNA inter- derm development LRP chaperone (MESD) were verified as the actions, Homo sapiens (hsa)‑miR‑185‑3p‑heat shock protein upregulated genes, and ITPR interacting domain containing 2 family A member 12B (HSPA12B) and hsa‑miR185‑3p‑GRB (ITPRID2) and Inka box actin regulator 2 (INKA2) as the associated binding protein 2 (GAB2), were excluded due to a downregulated genes in IPF. The detailed expression levels lack of biological plausibility (the miRNA and mRNA were of these 5 genes in each group were plotted and compared upregulated). The remaining 3 genes with miRNA‑mRNA in Fig. 5. interactions were subsequently verified in a second miRNA predicting database, TargetScan. The results identified Identification of dysregulated genes with potential hsa‑miR‑185‑3p‑TRNA‑YW synthesizing protein 1 homolog miRNA‑mRNA interactions in IPF fibroblasts. Venn diagram (TYW1), hsa‑miR‑3662‑​glycoprotein M6B (GPM6B), INTERNATIONAL JOURNAL OF MOlecular medicine 43: 1643-1656, 2019 1651

Table IV. Gene Expression Omnibus verification of 42 dysregulated genes in IPF fibroblasts.

A, Upregulated genes

GSE44723 GSE24206 ‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑ ‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑ Genes Fold‑change Up-/Downregulation P‑value Up-/Downregulation P‑value

HSPA12B 18.15 N/A ‑ Up 0.835 NTM 13.94 Up 0.257 Up 0.010 PAX8 6.35 Up 0.341 Up 0.032 MKI67 5.15 Down 0.582 Up 0.017 POSTN 5.14 Down 0.688 Up 0.008 MYO1C 5.04 Down 0.810 Up 0.259 LINC01529 3.24 N/A ‑ N/A ‑ COL1A2 2.97 Down 0.397 Up <0.001 CCNL1 2.88 Up 0.737 Down <0.001 ATP2B4 2.78 Up 0.147 Up 0.159 ANP32B 2.74 Up 0.443 Down 0.159 PKMYT1 2.69 Down 0.929 Up 0.832 GAB2 2.63 Down 0.873 Down <0.001 GPM6B 2.57 Up 0.890 Down 0.013 TDG 2.45 N/A ‑ Down 0.113 HSPA8 2.40 N/A ‑ Up 0.081 CD248 2.32 Down 0.255 Up 0.115 LINC00847 2.19 Down 0.053 Up 0.091 ANKRD18EP 2.17 N/A ‑ N/A ‑ CNN3 2.11 N/A ‑ Down 0.095 MESD 2.09 Up 0.958 Up 0.002 IQGAP1 2.02 Up 0.437 Down 0.717 LINC01291 2.01 N/A ‑ N/A ‑

B, Downregulated genes

TYW1 ‑6.06 Up 0.201 Down 0.716 COL4A1 ‑5.81 Down 0.224 Up 0.354 AKAP12 ‑5.45 Up 0.410 Down 0.004 ITPRID2 ‑4.82 Down 0.019 Down 0.031 PRKAR1B ‑4.33 Down 0.194 Up 0.767 INKA2 ‑4.14 Down 0.213 Down 0.001 AKAP2 ‑3.88 N/A ‑ Down 0.007 GPR17 ‑3.51 N/A ‑ Down 0.460 FAM151A ‑3.15 N/A ‑ Down 0.081 UXT‑AS1 ‑3.10 N/A ‑ N/A ‑ IL17RC ‑2.88 Down 0.841 Down 0.630 SPDYE6 ‑2.88 N/A ‑ Down 0.220 MED31 ‑2.73 Up 0.478 Up 0.831 FEM1B ‑2.38 Up 0.873 Down 0.024 NCL ‑2.35 N/A ‑ Down 0.166 PTX3 ‑2.19 Up 0.918 Down <.001 OR7E12P ‑2.12 N/A ‑ Down 0.937 SPON1 ‑2.06 Up 0.986 Down 0.036 COL4A2 ‑2.01 Down 0.214 Up 0.584

Fold‑change indicates the results of RNA‑seq analysis from the next‑generation sequencing data. GEO, Gene Expression Omnibus; IPF, idiopathic pulmonary fibrosis; N/A, not available. 1652 SHEU et al: BIOINFORMATIC ANALYSIS OF DYSREGULATED GENES IN IPF FIBROBLASTS

Table V. Dysregulated genes with potential miRNA‑mRNA interaction in IPF fibroblasts.

Gene mRNA miRNA miRmap TargetScan Gene full name fold‑change miRNA fold‑change score Total context++ score

TYW1 tRNA‑YW synthesizing ‑6.06 hsa‑miR‑185‑3p 2.06 99.12 ‑0.37 protein 1 homolog GPM6B glycoprotein M6B 2.44 hsa‑miR‑3662 ‑3.37 99.81 ‑0.09 INKA2 Inka box actin regulator 2 ‑4.14 hsa‑miR‑1254 2.38 99.28 ‑0.32 hsa‑miR‑766‑3p 3.07 99.09 ‑0.67

IPF, idiopathic pulmonary fibrosis; miRNA, microRNA; hsa, Homo sapiens; TYW1, TRNA‑YW synthesizing protein 1 homolog; GPM6B, glycoprotein M6B; INKA2, Inka box actin regulator 2.

Figure 5. Gene Expression Omnibus analysis of 5 dysregulated genes in IPF fibroblasts. Representative microarray datasets GSE44723 (slowly and rapidly progressing IPF: cultured lung fibroblasts) and GSE24206 (early and advanced IPF: whole lung tissue) were used for verification of differential gene expres- sions identified from Next Generation Sequencing analysis. The results indicated that the expression of INKA2 and ITRPID2 were downregulated and PAX8, MESD and NTM were upregulated in IPF. Cultured lung fibroblasts and whole lung from healthy subjects were used as the normal controls. P‑values were calculated using the Wilcoxon rank‑sum test for comparisons of two groups, and the Kruskal‑Wallis test for comparisons of three groups. Adjusted P‑values were calculated using the Kruskal‑Wallis test followed by Benjamini‑Hochberg multiple‑testing corrections. *Adjusted P<0.05, **adjusted P<0.01 and ***adjusted P<0.001. IPF, idiopathic pulmonary fibrosis; INKA2, Inka box actin regulator 2; ITPRID2, ITPR interacting domain containing 2; PAX8, paired box 8; MESD, mesoderm development LRP chaperone; NTM, neurotrimin. INTERNATIONAL JOURNAL OF MOlecular medicine 43: 1643-1656, 2019 1653

Figure 6. Venn diagram analysis of miRNA‑mRNA interactions in idiopathic pulmonary fibrosis fibroblasts. RNA sequencing revealed 42 dysregulated genes (left). Small RNA sequencing revealed 60 dysregulated miRNAs, which predicted 1,908 target genes (right) based on miRmap database. Venn diagram analysis identified 5 dysregulated genes with potential miRNA‑mRNA interaction. miRNA, microRNA.

Figure 7. Proposed mechanisms demonstrating gene regulations of fibroblasts and their potential roles in IPF. Upregulation of hsa‑miR‑1254 and hsa‑miR‑766‑3p may inhibit INKA2 expression, leading to overexpression of PAK4, which may promote fibroblast proliferation and migration, and protect fibroblasts from apoptosis. Overexpression of PAX8 may also have a role in fibroblast proliferation. The functions of NTM, MESD and ITPRID2 remain unknown. Whether they contribute to the pro‑proliferative and pro‑fibrotic microenvironment requires additional study. Based on the results of the Gene Ontology analysis, the gene dysregulations in IPF fibroblasts may also have a role in the excessive production and deposition of disorganized ECM. The hsa‑miR‑1254‑INKA2 and hsa‑miR‑766‑3p‑INKA2 interactions were identified based on bioinformatic analysis, and therefore requires additional experi- ments to confirm. IPF, idiopathic pulmonary fibrosis; hsa, Homo sapiens; INKA2, Inka box actin regulator 2; PAK4, serine/threonine‑protein kinase PAK4; PAX8, paired box 8; MESD, mesoderm development LRP chaperone; NTM, neurotrimin; ITPRID2, ITPR interacting domain containing 2; ECM, extracellular matrix.

hsa‑miR‑1254‑INKA2 and hsa‑miR‑766‑3p‑INKA2 as and miRNA profiles of IPF fibroblasts were obtained using an the potential miRNA‑mRNA interactions in IPF fibro- NGS platform, and bioinformatics analyses were performed in blasts (Table V). a step‑wise manner. A total of 42 dysregulated genes were iden- tified, and then bioinformatics tools IPA and DAVID were used Discussion for pathway and functional analyses. A total of 5 of the differen- tially expressed genes were verified in the representative GEO The pathogenic mechanisms of IPF have not yet been fully datasets, including NTM, PAX8 and MESD (upregulated), and elucidated. The hallmarks of IPF are aberrant activation of ITPRID2 and INKA2 (downregulated). Integrated analysis lung epithelial cells, accumulation of fibroblasts and myofibro- of mRNA and miRNA expression data was also performed, blasts, and excessive production of ECM (4,14‑16). Previous and hsa‑miR‑185‑3p‑TYW1, hsa‑miR‑3662‑GPM6B, data indicate that fibroblasts, as one of the key cells, serve a hsa‑miR‑1254‑INKA2 and hsa‑miR‑766‑3p‑INKA2 were crucial role in the development and progression of IPF (16). identified as the potential miRNA‑mRNA interactions An improved understanding of the gene regulation in IPF in IPF fibroblasts. According to the GEO verification, fibroblasts may assist in the development of novel therapeutics hsa‑miR‑1254‑INKA2 and hsa‑miR‑766‑3p‑INKA2 were targeting this key cell. In the present study, the whole mRNA considered as the most likely dysregulated miRNA‑mRNA 1654 SHEU et al: BIOINFORMATIC ANALYSIS OF DYSREGULATED GENES IN IPF FIBROBLASTS interactions in IPF fibroblasts. However, these interactions but undetectable in normal lung fibroblasts. Based on the quality were identified based on bioinformatic analysis. Therefore, control criterion to exclude genes with FPKM <0.3 in either they require additional experiments to confirm the results. IPF or normal fibroblasts, PAK4 was not able to be analyzed in Hsa‑miR185‑3p‑HSPA12B and hsa‑miR185‑3p‑GAB2 were the present study. The upregulation of PAK4 in IPF fibroblasts excluded from subsequent analysis, as the miRNAs and required additional confirmation. It was also identified that the mRNAs were dysregulated in the same manner. There is a miRNAs hsa‑miR‑1254 and hsa‑miR‑766‑3p, 2 putative regula- possibility of indirect modulation, in that the upregulated tors of INKA2, were upregulated in IPF fibroblasts. Therefore, hsa‑miR185‑3p may control one or more other targets. Which the results suggested that the miRNA‑mRNA interaction in the may in turn upregulate the expression levels of HSPA12B INKA2 gene may serve a role in cell growth, proliferation or or GAB2. Nevertheless, the upregulated levels of HSPA12B apoptosis inhibition in IPF fibroblasts. or GAB2 were not validated in the GEO database analysis. The PAX8 gene encodes protein PAX8, which is a member Whether these 2 miRNA‑mRNA interactions were excluded of the paired box family of transcription factors. PAX8 is or not did not affect the final results. involved in the development and differentiation of thyroid In the GO analysis, it was identified that the most important follicular cells (56,57), and is associated with the growth function of the identified dysregulated genes was associated of cancer cells (58,59). Overexpression of PAX8 has been with the composition and function of the ECM. Replacement observed in a number of types of cancer. It also has a critical of the normal lung structure with an excessive deposition of role in the survival and proliferation of epithelial cells (60). In disorganized collagen and ECM is the hallmark of IPF (40). the present study, upregulation of PAX8 was identified in IPF Although previous evidence suggests that fibroblasts and fibroblasts. These data suggested that PAX8 may serve a role in myofibroblasts in the fibrotic foci are the key cells leading to promoting the growth, survival or proliferation of fibroblasts excessive ECM production (41), the crosstalk between epithe- in IPF. The upregulation of NTM and MESD (also known as lial cells, fibroblasts, myofibroblasts and ECM remains largely mesoderm development candidate 2), and downregulation of uncharacterized. The results from the present study improve the ITPRID2 (also known as Ki‑Ras‑induced actin‑interacting understanding of the fibroblast‑ECM interaction in the patho- protein or sperm specific antigen 2) were also identified in genesis of IPF. The development of novel therapeutic strategies IPF fibroblasts. Neurotrimin, a neural cell adhesion molecule, targeting the fibrotic ECM may provide an opportunity to may promote neurite outgrowth and adhesion via a homo- halt fibrosis and restore organ function (42). A recent study philic mechanism (61,62). In addition to brain tissue, human confirmed the importance of the ECM in IPF pathogenesis and tissue‑specific expression analysis indicated a high expres- treatment: Kwapiszewska et al (43) compared transcriptomic sion of NTM in human lung tissue (63). MESD, a specialized profiles in lung homogenates and fibroblasts obtained from chaperone for low‑density lipoprotein receptor‑related protein patients with IPF treated with or without pirfenidone. They (LRP)5 and LRP6, is a universal inhibitor of Wnt/LRP identified that cell migration‑inducing and hyaluronan‑binding signaling on the cell surface (64,65). ITPRID2 encodes a protein (CEMIP) was markedly downregulated by pirfenidone protein that is involved in the regulation of filamentous actin treatment. They also identified that circulating CEMIP levels and extracellular signals (66), and may be associated with were significantly increased in patients with IPF compared structural integrity and/or signal transductions in human with the healthy controls, and that pirfenidone treatment cancer (67). The functions of NTM, MESD and ITPRID2 was associated with a significant decrease in CEMIP levels. remain largely unknown. Their associations with human lung CEMIP has been previously associated with ECM production, diseases, particularly IPF, require additional investigation. inflammation and cell proliferation (44,45). They concluded The identified gene dysregulation in fibroblasts and their that pirfenidone exhibited effects on multiple pathways in proposed mechanisms in IPF are summarized in Fig. 7. In fibroblasts and other pulmonary cells, through the regulation summary, the present study demonstrated that NTM, PAX8 of the ECM structure and inflammatory reactions. and MESD were upregulated and ITPRID2 and INKA2 were The INKA2 gene, also known as family with sequence simi- downregulated in IPF fibroblasts. Dysregulation of PAX8 and larity 212 member B or 1 open reading frame 183, INKA2 may participate in the regulation of proliferation and encodes the protein serine/threonine‑protein kinase PAK4 survival in IPF fibroblasts. Functional analysis of the dysregu- (PAK4)‑inhibitor INKA2. The PAK4‑inhibitor INKA proteins, lated genes suggested a marked association between fibroblasts with 2 isoforms in humans, are endogenous inhibitors of and ECM. The integrated analysis of miRNA and mRNA PAK4 (46). The PAK proteins are a family of serine/threonine expression profiles suggested that the upregulated miRNAs cyclin‑dependent kinase inhibitor 1‑activating protein kinases hsa‑miR‑1254 and hsa‑miR‑766‑3p may downregulate INKA2 and have been implicated in a range of biological activities (46). and serve important roles in IPF. These data improve the PAK4 is an effector molecule for the Rho GTPase cell division current understanding of fibroblasts as key cells in the patho- control protein 42 homolog. Previous experiments have indicated genesis of IPF. As a screening study using bioinformatics that active PAK4 protects cells from apoptosis (47,48). PAK4 is approaches, without biological or cross‑platform replicates, associated with tumorigenesis and progression in different types the results of the present study require additional validation. of cancer through promoting cell growth, proliferation, migra- tion and metastasis (49‑55). The downregulation of INKA2 Acknowledgements identified in the present study may potentially upregulate the expression of PAK4, which in turn promotes cell proliferation The authors gratefully acknowledge the assistance from the and inhibits cell apoptosis in IPF fibroblasts. The NGS data Center for Research and Development of Kaohsiung Medical suggested that the FPKM of PAK4 was 0.2922 in IPF fibroblasts, University (Kaohsiung, Taiwan), and the assistance with INTERNATIONAL JOURNAL OF MOlecular medicine 43: 1643-1656, 2019 1655 statistical analysis from Mr. Tse‑Kuang Kai at Kaohsiung 8. Baumgartner KB, Samet JM, Coultas DB, Stidley CA, Hunt WC, Colby TV and Waldron JA: Occupational and environmental Medical University Hospital. risk factors for idiopathic pulmonary fibrosis: A multicenter case‑control study. Collaborating centers. Am J Epidemiol 152: Funding 307‑315, 2000. 9. Tang YW, Johnson JE, Browning PJ, Cruz‑Gervis RA, Davis A, Graham BS, Brigham KL, Oates JA Jr, Loyd JE and The present study was supported in part by research Stecenko AA: Herpesvirus DNA is consistently detected in grants from the Ministry of Science and Technology lungs of patients with idiopathic pulmonary fibrosis. J Clin Microbiol 41: 2633‑2640, 2003. (grant nos. MOST 107‑2320‑B‑037‑011‑MY3 and MOST 10. Tobin RW, Pope CE II, Pellegrini CA, Emond MJ, Sillery J and 106‑2314‑B‑037‑016‑MY2), the Kaohsiung Medical University Raghu G: Increased prevalence of gastroesophageal reflux in Hospital Research Foundation (grant nos. KMUHS10701, patients with idiopathic pulmonary fibrosis. Am J Respir Crit Care Med 158: 1804‑1808, 1998. KMUHS10712, KMUH106‑6R15 and KMUH107‑7M06), and 11. Fingerlin TE, Murphy E, Zhang W, Peljto AL, Brown KK, the Kaohsiung Medical University (grant no. KMU‑DK108003). Steele MP, Loyd JE, Cosgrove GP, Lynch D, Groshong S, et al: Genome‑wide association study identifies multiple susceptibility loci for pulmonary fibrosis. Nat Genet 45: 613‑620, 2013. Availability of data and materials 12. Noth I, Zhang Y, Ma SF, Flores C, Barber M, Huang Y, Broderick SM, Wade MS, Hysi P, Scuirba J, et al: Genetic vari- The data used and analyzed in this study are available from the ants associated with idiopathic pulmonary fibrosis susceptibility and mortality: A genome‑wide association study. Lancet Respir corresponding author on reasonable request. Med 1: 309‑317, 2013. 13. Peljto AL, Zhang Y, Fingerlin TE, Ma SF, Garcia JG, Richards TJ, Authors' contributions Silveira LJ, Lindell KO, Steele MP, Loyd JE, et al: Association between the MUC5B promoter polymorphism and survival in patients with idiopathic pulmonary fibrosis. JAMA 309: CCS, IWC and PLK conceived the study. CCS, WAC and MJT 2232‑2239, 2013. analyzed and interpreted the data. CCS, WAC, MJT, IWC and 14. Mora AL, Rojas M, Pardo A and Selman M: Emerging thera- pies for idiopathic pulmonary fibrosis, a progressive age‑related PLK prepared the manuscript. SHL performed cell culture disease. Nat Rev Drug Discov 16: 755‑772, 2017. and laboratory experiments. All authors read and approved the 15. Gomperts BN and Strieter RM: Fibrocytes in lung disease. final manuscript. J Leukoc Biol 82: 449‑456, 2007. 16. Pardo A and Selman M: Lung fibroblasts, aging, and idiopathic pulmonary fibrosis. Ann Am Thorac Soc 13 (Suppl 5): S417‑S421, Ethics approval and consent to participate 2016. 17. Ramos C, Montaño M, García‑Alvarez J, Ruiz V, Uhal BD, Selman M and Pardo A: Fibroblasts from idiopathic pulmonary Not applicable. fibrosis and normal lungs differ in growth rate, apoptosis, and tissue inhibitor of metalloproteinases expression. Am J Respir Patient consent for publication Cell Mol Biol 24: 591‑598, 2001. 18. Pierce EM, Carpenter K, Jakubzick C, Kunkel SL, Evanoff H, Flaherty KR, Martinez FJ, Toews GB and Hogaboam CM: Not applicable. Idiopathic pulmonary fibrosis fibroblasts migrate and proliferate to CC chemokine ligand 21. Eur Respir J 29: 1082‑1093, 2007. 19. Kurundkar AR, Kurundkar D, Rangarajan S, Locy ML, Zhou Y, Competing interests Liu RM, Zmijewski J and Thannickal VJ: The matricellular protein CCN1 enhances TGF‑β1/SMAD3‑dependent profibrotic The authors declare that they have no competing interests. signaling in fibroblasts and contributes to fibrogenic responses to lung injury. FASEB J 30: 2135‑2150, 2016. 20. Conte E, Gili E, Fruciano M, Korfei M, Fagone E, Iemmolo M, References Lo Furno D, Giuffrida R, Crimi N, Guenther A and Vancheri C: PI3K p110γ overexpression in idiopathic pulmonary fibrosis lung 1. Raghu G, Remy‑Jardin M, Myers JL, Richeldi L, Ryerson CJ, tissue and fibroblast cells: In vitro effects of its inhibition. Lab Lederer DJ, Behr J, Cottin V, Danoff SK, Morell F, et al: Invest 93: 566‑576, 2013. Diagnosis of idiopathic pulmonary fibrosis. An official 21. Vuga LJ, Ben‑Yehudah A, Kovkarova‑Naumovski E, Oriss T, ATS/ERS/JRS/ALAT clinical practice guideline. Am J Respir Gibson KF, Feghali‑Bostwick C and Kaminski N: WNT5A is a Crit Care Med 198: e44‑e68, 2018. regulator of fibroblast proliferation and resistance to apoptosis. 2. Raghu G, Collard HR, Egan JJ, Martinez FJ, Behr J, Brown KK, Am J Respir Cell Mol Biol 41: 583‑589, 2009. Colby TV, Cordier JF, Flaherty KR, Lasky JA, et al: An official 22. Desai O, Winkler J, Minasyan M and Herzog EL: The role of ATS/ERS/JRS/ALAT statement: Idiopathic pulmonary fibrosis: immune and inflammatory cells in idiopathic pulmonary fibrosis. Evidence‑based guidelines for diagnosis and management. Am J Front Med 5: 43, 2018. Respir Crit Care Med 183: 788‑824, 2011. 23. Vejnar CE and Zdobnov EM: MiRmap: Comprehensive predic- 3. Vancheri C, Failla M, Crimi N and Raghu G: Idiopathic pulmo- tion of microRNA target repression strength. Nucleic Acids nary fibrosis: A disease with similarities and links to cancer Res 40: 11673‑11683, 2012. biology. Eur Respir J 35: 496‑504, 2010. 24. Agarwal V, Bell GW, Nam JW and Bartel DP: Predicting effec- 4. Lederer DJ and Martinez FJ: Idiopathic pulmonary fibrosis. tive microRNA target sites in mammalian mRNAs. Elife 4, 2015. N Engl J Med 378: 1811‑1823, 2018. 25. Sheu CC, Tsai MJ, Chen FW, Chang KF, Chang WA, Chong IW, 5. Hutchinson J, Fogarty A, Hubbard R and McKeever T: Global Kuo PL and Hsu YL: Identification of novel genetic regula- incidence and mortality of idiopathic pulmonary fibrosis: A tions associated with airway epithelial homeostasis using systematic review. Eur Respir J 46: 795‑806, 2015. next‑generation sequencing data and bioinformatics approaches. 6. Navaratnam V, Fogarty AW, Glendening R, McKeever T and Oncotarget 8: 82674‑82688, 2017. Hubbard RB: The increasing secondary care burden of idiopathic 26. Chang WA, Tsai MJ, Jian SF, Sheu CC and Kuo PL: Systematic pulmonary fibrosis: Hospital admission trends in England from analysis of transcriptomic profiles of COPD airway epithelium 1998 to 2010. Chest 143: 1078‑1084, 2013. using next‑generation sequencing and bioinformatics. Int J Chron 7. Sack C, Vedal S, Sheppard L, Raghu G, Barr RG, Podolanczuk A, Obstruct Pulmon Dis 13: 2387‑2398, 2018. Doney B, Hoffman EA, Gassett A, Hinckley‑Stukovsky K, et al: 27. Tsai MJ, Chang WA, Jian SF, Chang KF, Sheu CC and Kuo PL: Air pollution and subclinical interstitial lung disease: The Possible mechanisms mediating apoptosis of bronchial epithelial Multi‑Ethnic Study of Atherosclerosis (MESA) air‑lung study. cells in chronic obstructive pulmonary disease ‑ A next‑genera- Eur Respir J 50: 1700559, 2017. tion sequencing approach. Pathol Res Pract 214: 1489‑1496, 2018. 1656 SHEU et al: BIOINFORMATIC ANALYSIS OF DYSREGULATED GENES IN IPF FIBROBLASTS

28. Bolger AM, Lohse M and Usadel B: Trimmomatic: A flex- 50. Siu MK, Chan HY, Kong DS, Wong ES, Wong OG, Ngan HY, ible trimmer for Illumina sequence data. Bioinformatics 30: Tam KF, Zhang H, Li Z, Chan QK, et al: p21‑activated kinase 4 2114‑2120, 2014. regulates ovarian cancer cell proliferation, migration, and inva- 29. Friedländer MR, Mackowiak SD, Li N, Chen W and Rajewsky N: sion and contributes to poor prognosis in patients. Proc Natl miRDeep2 accurately identifies known and hundreds of novel Acad Sci USA 107: 18622‑18627, 2010. microRNA genes in seven animal clades. Nucleic Acids Res 40: 51. Wong LE, Chen N, Karantza V and Minden A: The Pak4 protein 37‑52, 2012. kinase is required for oncogenic transformation of MDA‑MB‑231 30. Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, breast cancer cells. Oncogenesis 2: e50, 2013. Zahler AM and Haussler D: The human genome browser at 52. Guo Q, Su N, Zhang J, Li X, Miao Z, Wang G, Cheng M, Xu H, UCSC. Genome Res 12: 996‑1006, 2002. Cao L and Li F: PAK4 kinase‑mediated SCG10 phosphorylation 31. Kim D, Langmead B and Salzberg SL: HISAT: A fast spliced involved in gastric cancer metastasis. Oncogene 33: 3277‑3287, aligner with low memory requirements. Nat Methods 12: 2014. 357‑360, 2015. 53. Wang C, Li Y, Zhang H, Liu F, Cheng Z, Wang D, Wang G, Xu H, 32. Galipon J, Ishii R, Suzuki Y, Tomita M and Ui‑Tei K: Differential Zhao Y, Cao L, et al: Oncogenic PAK4 regulates Smad2/3 axis binding of three major human ADAR isoforms to coding and involving gastric tumorigenesis. Oncogene 33: 3473‑3484, 2014. long non‑coding transcripts. Genes 8: E68, 2017. 54. Xia M, Wei J and Tong K: MiR‑224 promotes proliferation 33. Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, and migration of gastric cancer cells through targeting PAK4. Pimentel H, Salzberg SL, Rinn JL and Pachter L: Differential Pharmazie 71: 460‑464, 2016. gene and transcript expression analysis of RNA‑seq experiments 55. Zhang X, Zhang X, Li Y, Shao Y, Xiao J, Zhu G and Li F: PAK4 with TopHat and Cufflinks. Nat Protoc 7: 562‑578, 2012. regulates G6PD activity by p53 degradation involving colon 34. Krämer A, Green J, Pollard J Jr and Tugendreich S: Causal analysis cancer cell growth. Cell Death Dis 8: e2820, 2017. approaches in ingenuity pathway analysis. Bioinformatics 30: 56. Pasca di Magliano M, Di Lauro R and Zannini M: Pax8 has a key 523‑530, 2014. role in thyroid cell differentiation. Proc Natl Acad Sci USA 97: 35. Huang da W, Sherman BT and Lempicki RA: Systematic and 13144‑13149, 2000. integrative analysis of large gene lists using DAVID bioinfor- 57. Mansouri A, Chowdhury K and Gruss P: Follicular cells of the matics resources. Nat Protoc 4: 44‑57, 2009. thyroid gland require Pax8 gene function. Nat Genet 19: 87‑90, 36. Huang da W, Sherman BT and Lempicki RA: Bioinformatics 1998. enrichment tools: Paths toward the comprehensive functional 58. Muratovska A, Zhou C, He S, Goodyer P and Eccles MR: analysis of large gene lists. Nucleic Acids Res 37: 1‑13, 2009. Paired‑Box genes are frequently expressed in cancer and often 37. Edgar R, Domrachev M and Lash AE: Gene Expression required for cancer cell survival. Oncogene 22: 7989‑7997, 2003. Omnibus: NCBI gene expression and hybridization array data 59. Ghannam‑Shahbari D, Jacob E, Kakun RR, Wasserman T, repository. Nucleic Acids Res 30: 207‑210, 2002. Korsensky L, Sternfeld O, Kagan J, Bublik DR, Aviel‑Ronen S, 38. Meltzer EB, Barry WT, D'Amico TA, Davis RD, Lin SS, Levanon K, et al: PAX8 activates a p53‑p21‑dependent Onaitis MW, Morrison LD, Sporn TA, Steele MP and Noble PW: pro‑proliferative effect in high grade serous ovarian carcinoma. Bayesian probit regression model for the diagnosis of pulmonary Oncogene 37: 2213‑2224, 2018. fibrosis: Proof‑of‑principle. BMC Med Genomics 4: 70, 2011. 60. Di Palma T, Filippone MG, Pierantoni GM, Fusco A, Soddu S 39. Peng R, Sridhar S, Tyagi G, Phillips JE, Garrido R, Harris P, and Zannini M: Pax8 has a critical role in epithelial cell survival Burns L, Renteria L, Woods J, Chen L, et al: Bleomycin induces and proliferation. Cell Death Dis 4: e729, 2013. molecular changes directly relevant to idiopathic pulmonary 61. Struyk AF, Canoll PD, Wolfgang MJ, Rosen CL, D'Eustachio P fibrosis: A model for ‘active’ disease. PLoS One 8: e59348, 2013. and Salzer JL: Cloning of neurotrimin defines a new subfamily 40. Barratt SL, Creamer A, Hayton C and Chaudhuri N: Idiopathic of differentially expressed neural cell adhesion molecules. pulmonary fibrosis (IPF): An overview. J Clin Med 7: E201, 2018. J Neurosci 15: 2141‑2156, 1995. 41. Sgalla G, Iovene B, Calvello M, Ori M, Varone F and Richeldi L: 62. Gil OD, Zanazzi G, Struyk AF and Salzer JL: Neurotrimin medi- Idiopathic pulmonary fibrosis: Pathogenesis and management. ates bifunctional effects on neurite outgrowth via homophilic Respir Res 19: 32, 2018. and heterophilic interactions. J Neurosci 18: 9312‑9325, 1998. 42. Walraven M and Hinz B: Therapeutic approaches to control 63. Fagerberg L, Hallström BM, Oksvold P, Kampf C, Djureinovic D, tissue repair and fibrosis: Extracellular matrix as a game changer. Odeberg J, Habuka M, Tahmasebpoor S, Danielsson A, Matrix Biol 71‑72: 205‑224, 2018. Edlund K, et al: Analysis of the human tissue‑specific expres- 43. Kwapiszewska G, Gungl A, Wilhelm J, Marsh LM, Thekkekara sion by genome‑wide integration of transcriptomics and Puthenparampil H, Sinn K, Didiasova M, Klepetko W, ‑based proteomics. Mol Cell Proteomics 13: 397‑406, Kosanovic D, Schermuly RT, et al: Transcriptome profiling 2014. reveals the complexity of pirfenidone effects in idiopathic 64. Lin C, Lu W, Zhai L, Bethea T, Berry K, Qu Z, Waud WR pulmonary fibrosis. Eur Respir J 52: 1800564, 2018. and Li Y: Mesd is a general inhibitor of different Wnt ligands 44. Soroosh A, Albeiroti S, West GA, Willard B, Fiocchi C and in Wnt/LRP signaling and inhibits PC‑3 tumor growth in vivo. de la Motte CA: Crohn's disease fibroblasts overproduce the FEBS Lett 585: 3120‑3125, 2011. novel protein KIAA1199 to create proinflammatory hyaluronan 65. Lu W, Liu CC, Thottassery JV, Bu G and Li Y: Mesd is a universal fragments. Cell Mol Gastroenterol Hepatol 2: 358‑368, e4, 2016. inhibitor of Wnt coreceptors LRP5 and LRP6 and blocks 45. Li L, Yan LH, Manoj S, Li Y and Lu L: Central role of CEMIP in Wnt/beta‑catenin signaling in cancer cells. Biochemistry 49: tumorigenesis and its potential as therapeutic target. J Cancer 8: 4635‑4643, 2010. 2238‑2246, 2017. 66. Inokuchi J, Komiya M, Baba I, Naito S, Sasazuki T and 46. Baskaran Y, Ang KC, Anekal PV, Chan WL, Grimes JM, Shirasawa S: Deregulated expression of KRAP, a novel gene Manser E and Robinson RC: An in cellulo‑derived structure of encoding actin‑interacting protein, in human colon cancer cells. PAK4 in complex with its inhibitor Inka1. Nat Commun 6: 8681, J Hum Genet 49: 46‑52, 2004. 2015. 67. Fujimoto T, Koyanagi M, Baba I, Nakabayashi K, Kato N, 47. Gnesutta N, Qu J and Minden A: The serine/threonine kinase Sasazuki T and Shirasawa S: Analysis of KRAP expression and PAK4 prevents caspase activation and protects cells from apop- localization, and genes regulated by KRAP in a human colon tosis. J Biol Chem 276: 14414‑14419, 2001. cancer cell line. J Hum Genet 52: 978‑984, 2007. 48. Gnesutta N and Minden A: Death receptor‑induced activation of initiator caspase 8 is antagonized by serine/threonine kinase PAK4. Mol Cell Biol 23: 7838‑7848, 2003. This work is licensed under a Creative Commons 49. Callow MG, Clairvoyant F, Zhu S, Schryver B, Whyte DB, Attribution-NonCommercial-NoDerivatives 4.0 Bischoff JR, Jallal B and Smeal T: Requirement for PAK4 in the International (CC BY-NC-ND 4.0) License. anchorage‑independent growth of human cancer cell lines. J Biol Chem 277: 550‑558, 2002.